Week 11 Flashcards
Cause Density Bias
- Tendency to overestimate relationship in cue and outcome
- Usually when the cue/cause happens frequently
Causation
A cause to a situation appears to exist but often does not
Outcome Density Bias
- Tendency to overestimate relationship in cue and outcome
- Usually when the cue/outcome happens frequently
Illusory Causal Judgement Errors
- Once illusory causal judgement is established it can be difficult to replace
- Even when new information explains outcomes better.
- Yarritu et al, 2015
Representativeness Heuristic
- Representativeness is when a characteristic is associated with another cue
E.g. Robert is quiet and wears glasses; he is probably an accountant not a surfer
Base Rate
Relative proportion of different classes in the population
Base Rate Neglect
- When presented with base rate information people guess odds correctly
- Adding descriptions can increase estimates that are not correct
Inverse Base Rate Effect
- Medin & Edelson 1988
- Participants asked to diagnose disease based on symptom of pairs
- A & B had disease 01, A & C had disease 02
- Disease 01 was common & 02 was rare
- After learning contingencies asked to diagnose
- Tended to diagnose rare disease even though it was less likely
Conjunction Rule
The probability of two connected events/features cannot be higher than it’s single features
Law of Large Numbers
The larger the number of individuals chosen from a population the more they will come to represent a population
e.g. Small/Large Hospital
Myside Bias
Tendency for people to test and measure ideas based on their own opinions
Lord & Coworkers 1979
Confirmation Bias
Tendency to select information that already agrees with what we believe.
Overlooking information that contradicts it
Wason, 1960
Evaluating False Evidence
- Even if your reasoning has no error you can still come to the wrong conclusion if your facts are wrong
- People don’t usually evaluate evidence and rely heavily on inaccurate information
- Wineburg et al 2016
- Flowers with nuclear birth defects from Japan
The Backfire Effect
- Nyhan & Reifler 2010
- Study of misperception about Iraq’s Weapons of Mass Destruction
- Subjects given false news about WMD then some were corrected confirming no WMD
- Subjects made decisions differently depending on political affiliation
- Also differed if they had been corrected or not
- With very conservative’s political views belief became stronger even if they were corrected
Availability Heuristic
- Events more easily remembered are judged as more probable
- Occurs when easily remembered event is less probable
Illusory Correlation
- Strong correlation between two events appears but does not exist
- Happens when there is no/weak correlation
Representativeness Heuristic
- Probability that A is connected to B determined by how features of A resemble B
- Presence of similar properties don’t predict membership of class B
Base Rate
- Relative proportions of different types in a population
- When not taken into account errors in judgement occur
Conjunction Rate
- Probability of two features combined cannot be higher than each single feature
- Happens when higher probability is given to a conjunction pair
Law of Large Numbers
The more individuals chosen from a population, the more likely the sample represents the whole group.
Assumed when small number is taken and thought to be widespread
Myside Bias
- People tend to evaluate evidence based on their own beliefs
- A type of confirmation bias
- Happens when there is a narrow focus only on confirming information
Confirmation Bias
- Selectively looking for information that confirms belief
- Dismissing information that contradicts it
Backfire Effect
- Support for a viewpoint becomes stronger even if given facts the oppose their viewpoints
- Happens when people hold to their beliefs in the face of contradictory evidence
Judgement
The process of forming an opinion or conclusion
Inductive Reasoning
- Based on observation or reaching conclusions from evidence
- Conclusions are probably true, but not definitely true
- Science is based on Inductive Reasoning
- Have an idea
- Collect Data
- Draw a conclusion
Define Heuristics
- Rules of thumb likely to provide correct answer to a problem
- Are not Fool-Proof
- Availability Heuristic
- Representativeness Heuristic
Over Reliance on Heuristics can Lead To
- Illusory correlations and stereotypes
- Illusory causations
- Incorrect judgment of base rate
Cognitive Biases Could
- Hinder our abillity to gather evidence
- Distort our Judgement
- Can cause:
- Myside Bias
- Confirmation Bias
- Backfire Effect
Syllogisms are . . .
Valid if the conclusion follows logically for the premise
If two premise are true the conclusion must be true
True is hard to define but we assume it matches reality
Syllogism Validity
- Syllogisms can be invalid even when each premise and the conclusion seem reasonable.
- Is the reasoning behind syllogism 3 valid?
Belief Bias
Tendency to think a syllogism is valid if it’s conclusion is believable
Wason Four-Card Problem
- Effect of using real-word items for conditional reasoning
- Determine minimum number of cards to turn over to test
Real World - Four Card Problem
Performance improves in real-world terms
Griggs & Cox 1982
Not all Reasoning Tasks Require Abstract Code
- Language trained and untrained chimps reliably chose correct cone with the fruit in it
- Prior language training is not important for this task
- Same result is found with children over 4 years of age
Inverse Base Rate
- Subjects asked to diagnose disease based on symptom pairs
- Sometimes we pay attention to rarer outcomes
- This caused subjects to choose D02 even though it was considered rare.
Inverse Base Rate Rules
- When shown A&C or A&B people make the right choices because they fit the rule
- BUT when shown B&C together it does not fit the rule
- People choose the option that does not fit the rule because we tend to notice what is rare
- Known in medical field “diagnosing a zebra”
Belief Bias
- If the conclusion of a Syllogism is reasonable we tend to think its true.
- Here B = People, which could mean students
- But B = People might not all be students
- Therefore example is not valid
